This application addresses broad Challenge Area (01): Behavior, Behavioral Change, and Prevention, and specific Challenge Topic 03-MH-101: Biomarkers in Mental Disorders. Clinically significant depressive symptoms have been reported in up to 86% of nursing home residents [Brodaty01], with substantial under-recognition of the problem and excess morbidity and mortality in individuals with dementia. The sensitivity of current depression detection approaches deteriorates notably with progressive cognitive impairment due to: (i) diminishing ability of demented subjects to communicate affective states, (ii) critical overlap between the neurovegetative symptoms of depression and the neuropsychiatric features of dementia (e.g., apathy), and (iii) the transient and fluctuating nature of depressive symptoms in the context of dementia [Bielinski06]. In order to improve the assessment of depression in dementia patients, the NIMH has proposed modifications to the DSM-IV criteria for major depressive disorder that 1) highlight the transient and fluctuating nature of depressive symptoms, 2) addition of social withdrawal and irritability items, and 3) substitution of the anhedonia criterion with "decreased positive affect or pleasure in response to social contact and usual activities" [Olin02]. As the subject's ability to report internal affective states diminishes, however, activity and behavioral correlates of depression such as verbal and motor agitation, physical aggression, care resistance, and food/fluid refusal become important markers of possible depression [Bielinski06]. Emerging video, audio and sensor technologies hold promise for quantifying and automating the detection of such activities and behaviors, and thus identifying stages of depression that may be differentially responsive to various prevention and intervention strategies. We propose to overcome the under-diagnosis and failed recognition of symptoms of depression in dementia by applying real-time continuous video and audio recordings in the non-private spaces of a nursing home special care dementia unit (SCU;16 total beds), as well as sensor recordings (radiofrequency identification tags, motion, contact and pressure sensors) throughout all the SCU spaces to capture the consenting subjects'activities, behaviors and social interaction patterns. Current "gold standard" research rating instruments will also be administered to record a detailed account of the cohort's clinical characteristics. The recordings will take place in three two-week waves that are each separated by 3 months. A frame-by-frame annotation of the video recordings by trained coders, as well as the pattern of sensor recordings, will provide training data for computer-based machine learning algorithms that will automate the detection of potential activity and behavioral manifestations to distinguish depressed from non-depressed nursing home residents with cognitive impairment or dementia. PUBLIC HEALTH RELEVANCE: The technology developed and applied here may ultimately lead to automated assistance in elder care through more complete and accurate observational records for depression diagnosis and evaluation than possible now with limited staff in long-term care facilities and for elders living alone at home.